debian-mirror-gitlab/.gitlab/issue_templates/AI Project Proposal.md

Experiment

Problem to be solved

User problem

What user problem will this solve?

Solution hypothesis

Why do you believe this AI solution is a good way to solve this problem?

Assumption

What assumptions are you making about this problem and the solution?

Personas

What personas have this problem, who is the intended user?

Proposal

Success

How will you measure whether this experiment is a success?

General Availability

Main Job story

What job to be done will this solve?

Proposal updates/additions

Problem validation

What validation exists that customers have this problem?

Business objective

What business objective will be achieved with this proposal?

Confidence

Has this proposal been derived from research?

Confidence	Research
[High/Medium/Low]	research/insight issue

Requirements

What tasks or actions should the user be capable of performing with this feature?

⚠️ Related feature and research issues should be linked in the related issues section (Delete this line when this is done)

The user needs to be able to:

• ...
• ...
• ...

Checklist

Experiment

Issue information

• Add information to the issue body about:
  • The user problem being solved
  • Your assumptions
  • Who it's for, list of personas impacted
  • Your proposal
• Add relevant designs to the Design Management area of the issue if available
• Ensure this issue has the ~wg-ai-integration label to ensure visibility to various teams working on this

General Availability

Issue information

• Add information to the issue body about:
  • Your proposal
  • The Job Statement it's expected to satisfy
  • Details about the user problem and provide any research or problem validation
    • List the personas impacted by the proposal.
• Add all relevant solution validation issues to the Linked items section that shows this proposal will solve the customer problem, or details explaining why it's not possible to provide that validation.
• Add relevant designs to the Design Management area of the issue.
• You have adhered to our Definition of Done standards
• Ensure this issue has the ~wg-ai-integration label to ensure visibility to various teams working on this

Technical needs

• Operational Requirements Review - Checklist - #note_1337519985

1. Work estimate and skills needs to build an ML viable feature: To build any ML feature depending on the work, there are many personas that contribute including, Data Scientist, NLP engineer, ML Engineer, MLOps Engineer, ML Infra engineers, and Fullstack engineer to integrate the ML Services with Gitlab. Post-prototype we would assess the skills needed to build a production-grade ML feature for the prototype
2. Data Limitation: We would like to upfront validate if we have viable data for the feature including whether we can use the DataOps pipeline of ModelOps or create a custom one. We would want to understand the training data, test data, and feedback data to dial up the accuracy and the limitations of the data.
3. Model Limitation: We would want to understand if we can use an open-source pre-trained model, tune and customize it or start a model from scratch as well. Further, we would asses based on the ModelOps model evaluation framework which would be the right model to use based on the use case.
4. Cost, Scalability, Reliability: We would want to estimate the cost of hosting, serving, inference of the model, and the full end-to-end infrastructure including monitoring and observability.
5. Legal and Ethical Framework: We would want to align with legal and ethical framework like any other ModelOps features to cover across the nine principles of responsible ML and any legal support needed.

Dependency needs

• Operational Requirements Review - Checklist - #note_1337519985

Legal needs

• TBD

Additional resources

• If you'd like help with technical validation, or would like to discuss UX considerations for AI mention the AI Assisted group using @gitlab-org/modelops/applied-ml.
• Read about our AI Integration strategy
• Slack channels
  • #wg_ai_integration - Slack channel for the working group and the high level alignment on getting AI ready for Production (Development, Product, UX, Legal, etc.) But from the other channels fell free to reach out and post progress here
  • #ai_integration_dev_lobby - Channel for all implementation related topics and discussions of actual AI features (e.g. explain the code)
  • #ai_enablement_team - Channel for the AI Enablement Team which is building the base for all features (experimentation API, Abstraction Layer, Embeddings, etc.)

/label ~wg-ai-integration /cc @tmccaslin @hbenson @wayne @pedroms @jmandell /confidential

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